An overused chart, why it fails, and how to fix it

Reader and tipster Chris P. found this "death spiral" chart dizzying (link).

It's one of those charts that has conceptual appeal but does not do the data justice. As the name implies, the designer has a strong message, that the arctic sea ice volume has dramatically declined over time. This message is there in the chart but the reader has to work hard to find it.

Why doesn't this spider chart work? We can be more precise.

• A big problem is the lack of scalability. This chart looks different every year. If you add an extra year to the chart, you either have to increase the density of the years or you have to drop the earliest year.
• Years are not circular or periodic so the metaphor doesn't quite work.
• This chart type requires way too many gridlines.
• Axis labeling is also awkward. Because of the polar coordinates, the axes are radiating so the numbers run up toward the top but run down toward the bottom.
• This specific instance of spider chart benefits from the well-behaved data: the between-year variability is much lower than the within-year variability. As a result, the lines don't cross each other much. If the variability from year to year fluctuates a lot, we would have seen a bunch of noodles.

This is a pity because the designer did very well in aligning two corners of the Trifecta Checkup, namely what is the question and what does the data show? It is a great idea to control for month of year, and look at year to year changes. (A more typical view would be to look at month to month changes and plot one line per year.)

This is an example of a chart that does well on one side of the checkup but the failure is that the graph isn't in tune with the data or the question being addressed.

Whenever I see a spider chart, I want to unroll the spiral and see if a line chart is better. Thus:

The dramatic decrease in Arctic ice volume (no matter the month) is clear as day. You can actually read off the magnitude of the drop. (Try doing that in the spider chart, say between 1978 and 1995.)

This chart still has issues, namely too many colors. One can color the lines by season of the year, like this:

Or switch to a small-multiples set up with three lines per chart and one chart per season.

The seasonal arrangement is not arbitrary. You can see the effect of season by looking at side by side boxplots:

 The pattern is UP-DOWN-DOWN-UP.

In fact, a side-by-side boxplot of the data provides a very informative look:

The monthly series is obscured in this view, built into the vertical variability, which we can see is quite stable. The idea of controlling for month is to make it irrelevant. This view emphasizes the year on year decline of the entire distribution.

If you're worried that dropping too much information, the data can be grouped by season as before in a small-multiples setup like this:

Regardless of season, the trend is down.

 

PS. Alberto reminds me of his post about one example of a spider chart (radar chart) that works. Here's the link. It works because the graphical element is more in tune with the data. While the ice cap data has a linear trend over time, the voting data is all about differences in distribution. Also, the designer is expecting readers to care about the high-level pattern, not about the specifics.